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Course Outline
Day 1
- Data Science: an overview
- Practical part: Let’s get started with Python - Basic features of the language
- The data science life cycle - part 1
- Practical part: Working with structured data - the Pandas library
Day 2
- The data science life cycle - part 2
- Practical part: dealing with real data
- Data visualisation
- Practical part: the Matplotlib library
Day 3
- SQL - part 1
- Practical part: Creating a MySql database with tables, inserting data and performing simple queries
- SQL part 2
- Practical part: Integrating MySql and Python
Day 4
- Supervised learning part 1
- Practical part: regression
- Supervised learning part 2
- Practical part: classification
Day 5
- Supervised learning part 3
- Practical part: building a spam filter
- Unsupervised learning
- Practical part: Clustering images with k-means
Requirements
- An understanding of mathematics and statistics.
- Some programming experience, preferably in Python.
Audience
- Professionals interested in making a career change
- People curious about Data Science and Data Analytics
35 Hours
Testimonials (4)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
All the examples used and the lecturing style was on point even for a begginer i was able to understand and the training was so patient and always willing to go extra mile when in need of assistance.
Mathipa Chepape - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers
It is great to have the course custom made to the key areas that I have highlighted in the pre-course questionnaire. This really helps to address the questions that I have with the subject matter and to align with my learning goals.
Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra